Automunge solves the problem of data set encoding which in mainstream practice often requires a manual address. Further, the tool automates the prediction of missing data infill and may serve as a resource for non-deterministic inference by stochastic perturbations of tabular features.

— Nicholas Teague

Be sure to check out our paper Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge which was accepted to the 2022 ICML DataPerf workshop.

#to install to python:

pip install Automunge

#to import in notebook

from Automunge import *

am = AutoMunge()

Do you agree that Automunge is adding value to the machine learning ecosystem? If so there are several ways that your support would be appreciated:

• “Star” our GitHub repo

• “Like” and share one of our Medium essays

• Tell your friends and coworkers

• Follow us on twitter at @automunge

• Consider becoming a donor via Github Sponsors

Thank you, and once you try it out please let us know!

United States Patent Number 11861462